Abstract

Several studies have recently applied sentiment-based lexicons to Twitter to gauge local sentiment to understand health behaviors and outcomes for local areas. While this research has demonstrated the vast potential of this approach, lingering questions remain regarding the validity of Twitter mining and surveillance in local health research. First, how well does this approach predict health outcomes at very local scales, such as neighborhoods? Second, how robust are the findings garnered from sentiment signals when accounting for spatial effects? To evaluate these questions, we link 2,076,025 tweets from 66,219 distinct users in the city of San Diego over the period of 2014-12-06 to 2017-05-24 to the 500 Cities Project data and 2010–2014 American Community Survey data. We determine how well sentiment predicts self-rated mental health, sleep quality, and heart disease at a census tract level, controlling for neighborhood characteristics and spatial autocorrelation. We find that sentiment is related to some outcomes on its own, but these relationships are not present when controlling for other neighborhood factors. Evaluating our encoding strategy more closely, we discuss the limitations of existing measures of neighborhood sentiment, calling for more attention to how race/ethnicity and socio-economic status play into inferences drawn from such measures.

Highlights

  • Social media such as Twitter have introduced new methodologies for measuring health behaviors and outcomes

  • This study evaluates the singular impact of neighborhood sentiment as measured by social media by comparing the relation of an established method of identifying sentiment to neighborhood health outcomes, including self-rated mental health, sleep quality, and heart disease as exemplars

  • While the sentiment identified in Twitter has been linked with county-based health outcomes, existing studies are limited in several key ways

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Summary

Introduction

Social media such as Twitter have introduced new methodologies for measuring health behaviors and outcomes. Social media represent a relatively real-time large-scale snapshot of the messages, meanings and moods of a population. Every tweet is a signal of the sender’s state of mind and state of being at that moment. Every tweet is an attempt at influence on the receiver’s state of mind and state of being[1].

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